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<i title="Sakarya University Journal of Computer and Information Sciences">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/mwf7rb6aandftm6z2gzwoauage" style="color: black;">Sakarya University Journal of Computer and Information Sciences</a>
Recommender systems offer tailored recommendations by employing various algorithms, and collaborative filtering is one of the well-known and commonly used of those. A traditional collaborative filtering system allows users to rate on a single criterion. However, a single criterion may be insufficient to indicate preferences in domains such as restaurants, movies, or tourism. Multi-criteria collaborative filtering provides a multi-dimensional rating option. In similarity-based multi-criteria<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.35377/saucis...953348">doi:10.35377/saucis...953348</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/dpykkqqjjvbdnaghjq2ynyr3lm">fatcat:dpykkqqjjvbdnaghjq2ynyr3lm</a> </span>
more »... aborative filtering schemes, existing similarity methods utilize cousers or co-items regardless of how many there are. However, a high correlation with a few co-ratings does not always provide a reliable neighborhood. Therefore, it is very common, in both single-and multi-criteria collaborative filtering, to weight similarities with functions utilizing the number of co-ratings. Since multi-criteria collaborative filtering is yet growing, it lacks a comprehensive view of the effects of similarity weighting. This work studies multi-criteria collaborative filtering and the literature of binary vector similarities, which are frequently used for weighting, by giving a related taxonomy and conducts extensive experiments to analyze the effects of weighting similarities on item-and user-based multi-criteria collaborative filtering. Experimental findings suggest that prediction accuracy of item-based multi-criteria collaborative filtering can be boosted by especially binary vector similarity measures which do not consider mutual absences.
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